Principal Machine Learning Engineer

🔥 17 hours ago

🌐 United States, United Kingdom – Remote

🇺🇸 United States – Remote 🇬🇧 United Kingdom – Remote

⏰ Full Time

🔴 Lead

🤖 Machine Learning Engineer

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Accelerant

WebsiteLinkedInAll Job Openings

201 - 500 employees

Founded 2018

🛡️ Insurance

☁️ SaaS

🤝 B2B

💰 $150M Private Equity Round - Accelerant on 2023-06

Insurance • SaaS • B2B

Accelerant is a digital-first risk exchange and insurance platform that connects managing general agents (MGAs), underwriters, reinsurers, and institutional capital through a tech-powered, data-driven marketplace. The platform provides real-time analytics, underwriting tools, performance metrics, and operational support (actuarial, claims, regulatory) to streamline specialty insurance distribution and enable faster, more transparent capital deployment. Accelerant positions itself as a SaaS-style partner for specialty insurance firms, focused on improving efficiency, transparency, and profitable growth.

📋 Description

• Own the ML platform end to end, from data and feature pipelines through training infrastructure, model registry and lineage, inference services, and deployment • Design and build integrations with the wider Accelerant platform, third-party providers, and systems owned by other teams • Make deployment routine through versioning, staged rollout, rollback, and CI/CD for models and agents • Build monitoring that distinguishes data drift, pipeline breakage, and genuine performance decay, including when labels are delayed • Establish infrastructure for agentic AI, including orchestration, tool and API integration, retrieval, caching, and cost and latency controls • Own reliability, cost, and performance across ML workloads, from batch scoring to low-latency services • Build model governance and audit trails for regulators and internal risk committees • Lead and grow the function by setting technical standards, coaching a small team, and partnering with data scientists

🎯 Requirements

• Substantial experience running machine learning systems in production, including post-launch operations • Strong engineering foundations in Python, infrastructure as code, containers, and orchestration • Depth in at least one major cloud provider • Sound judgment about cost and failure modes • Data engineering capability across pipelines, orchestration, storage, and access patterns • Sufficient SQL proficiency to work effectively in a data warehouse • Experience integrating systems across organizational boundaries and influencing teams without direct authority • Statistical literacy sufficient to evaluate model performance with data scientists • Experience leading or coaching engineers • Judgment about infrastructure investment and operational value • Willingness to work with LLMs and agentic AI • Communication skills and credibility to determine when a system is not ready • Experience with LLM/agentic infrastructure, regulated industries, insurance or financial services, predictive modeling, internal platforms, real-time systems, streaming, feature stores, or high-throughput scoring is valuable

🏖️ Benefits

• Ownership of a function and freedom to decide how it works • A team of strong data scientists who need what you build • Problems spanning overnight batch scoring, low-latency services, and agentic systems • Collaborative group of people who enjoy solving difficult problems together • High autonomy and low bureaucracy